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loaded assay from Excel file ‘Metabolome_data.xlsx, sheet ’Metabolite_abundance_PRISM’
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loaded features annotations from Excel file ‘Metabolome_data.xlsx, sheet ’Metabolite_features’
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loaded samples annotations from Excel file ‘Metabolome_data.xlsx, sheet ’Metabolome_metadata_PRISM’
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Dataset info: 155 samples, 8848 features; 9 sample annotation fields, 8 feature annotation fields
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zeros replaced by NAs
Perform missingness analysis to determine if NAs significantly accumulate in one of the treatment groups. Adjust output of test using multiple testing correction.
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missingness analysis with variable Diagnosis
WARNING: Large data frame (3829 rows). Displaying first 1000 rows.
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P-value QQ plot for missingness
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Multiple testing correction of ‘missingness’ using ‘BH’
Plot sample boxplots before normalization, perform quotient normalization, plot boxplot with dilution factors from quotient normalization, plot sample boxplot after normalization, log transform the data, impute missing data using min value, plot sample boxplot after imputation, detect outliers, log dataset info, write pre-processed data to file.
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sample boxplot, aes: color=Diagnosis
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quotient normalization based on 34 reference samples and 1799 variables: ~
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quotient normalization based on 34 reference samples and 1799 variables: Diagnosis == “Control”
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dilution factor plot, ‘Diagnosis’
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sample boxplot, aes: color=Diagnosis
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log2.000000
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imputed via minimum value, 0 features with all NAs, returned as NAs
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sample boxplot, aes: color=Diagnosis
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flagged 0 univariate outliers
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Dataset info: 155 samples, 3829 features; 11 sample annotation fields, 8 feature annotation fields
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Filter samples: ~
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Filter samples: !is.na(Diagnosis)
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Transformed column ‘Diagnosis’ of sample annotations
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missingness plots, missing values: 0 out of 593495 (0.00%)
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features filtered, 50.00%, 0 of 3829 removed
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missingness plots, missing values: 0 out of 593495 (0.00%)